Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-28T04:11:40.535Z Has data issue: false hasContentIssue false
  • English
  • Français

Role of materials data science and informatics in accelerated materials innovation

Published online by Cambridge University Press:  02 August 2016

Surya R. Kalidindi ,
David B. Brough ,
Shengyen Li ,
Ahmet Cecen ,
Aleksandr L. Blekh ,
Faical Yannick P. Congo  and
Carelyn Campbell
Surya R. Kalidindi
Affiliation:
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA; [email protected]
David B. Brough
Affiliation:
School of Computational Science and Engineering, Georgia Institute of Technology, USA; [email protected]
Shengyen Li
Affiliation:
National Institute of Standards and Technology, USA; [email protected]
Ahmet Cecen
Affiliation:
School of Computational Science and Engineering, Georgia Institute of Technology, USA; [email protected]
Aleksandr L. Blekh
Affiliation:
George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA; [email protected]
Faical Yannick P. Congo
Affiliation:
Material Measurement Laboratory, Materials Science and Engineering Division, National Institute of Standards and Technology, USA; [email protected]
Carelyn Campbell
Affiliation:
Material Measurement Laboratory, Materials Science and Engineering Division, National Institute of Standards and Technology, USA; [email protected]
Get access

Abstract

The goal of the Materials Genome Initiative is to substantially reduce the time and cost of materials design and deployment. Achieving this goal requires taking advantage of the recent advances in data and information sciences. This critical need has impelled the emergence of a new discipline, called materials data science and informatics. This emerging new discipline not only has to address the core scientific/technological challenges related to datafication of materials science and engineering, but also, a number of equally important challenges around data-driven transformation of the current culture, practices, and workflows employed for materials innovation. A comprehensive effort that addresses both of these aspects in a synergistic manner is likely to succeed in realizing the vision of scaled-up materials innovation. Key toolsets needed for the successful adoption of materials data science and informatics in materials innovation are identified and discussed in this article. Prototypical examples of emerging novel toolsets and their functionality are described along with select case studies.

Keywords

microstructuremetalalloyinformaticsdata
Type
Research Article
Information
MRS Bulletin , Volume 41 , Issue 8: Microstructure Informatics in Process–Structure–Property Relations , August 2016 , pp. 596 - 602
Copyright
Copyright © Materials Research Society 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

“Materials Genome Initiative for Global Competitiveness” (National Science and Technology Council, 2011), http://www.whitehouse.gov/sites/default/files/microsites/ostp/materials_genome_initiative-final.pdf.Google Scholar
Committee on Integrated Computational Materials Engineering, National Materials Advisory Board, Division of Engineering and Physical Sciences, National Research Council, “Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security” (The National Academies Press, Washington, DC, 2008).Google Scholar
“A National Strategic Plan for Advanced Manufacturing” (National Science and Technology Council Committee on Technology Interagency Working Group on Advanced Manufacturing, February 2012), http://www.whitehouse.gov/sites/default/files/microsites/ostp/iam_advancedmanufacturing_strategicplan_2012.pdf.Google Scholar
Schmitz, G.J., Prahl, U., Integr. Mater. Manuf. Innov. 3, 2 (2014).Google Scholar
European Materials Modeling Council, http://emmc.info (accessed March 6, 2016).Google Scholar
Hale, J., Aero 4, 17 (2006).Google Scholar
Eager, T.W., Technol. Rev. 90, 24 (1987).Google Scholar
“The Materials Genome Initiative Strategic Plan” (Materials Genome Initiative National Science and Technology Council Committee on Technology Subcommittee on the Materials Genome Initiative, 2014), http://www.nist.gov/mgi/upload/MGI-StrategicPlan-2014.pdf.Google Scholar
“Implementing ICME in the Aerospace, Automotive, and Maritime Industries” (The Minerals, Metals and Materials Society, Warrendale, PA, 2013).Google Scholar
ASM International, “Materials Data Analytics: A Path-Finding Workshop” (The Ohio State University, October 8–9, 2015), http://www.asminternational.org/documents/10192/25925847/1-MDAHenry+Intro+2015-10-08.pdf/a67c84f5-4f44-48e3-b096-5a70352b338a (accessed March 6, 2016).Google Scholar
Georgia Tech, University of Wisconsin–Madison, University of Michigan, “Building an Integrated MGI Accelerator Network” (MGI Accelerator Workshop, June 5–6, 2014), http://acceleratornetwork.org/events/past-events/building-an-integrated-mgi-accelerator-network (accessed March 6, 2016).Google Scholar
“Modeling Across Scales: A Roadmapping Study for Connecting Materials Models and Simulations Across Length and Time Scales” (The Minerals, Metals and Materials Society, Warrendale, PA, 2015).Google Scholar
Kalidindi, S.R., Int. Mater. Rev. 60, 150 (2015).CrossRefGoogle Scholar
Kalidindi, S.R., Graef, M.D., Annu. Rev. Mater. Res. 45, 171–193 (2015).Google Scholar
Kalidindi, S.R., Hierarchical Materials Informatics (Butterworth-Heinemann, Oxford, 2015).Google Scholar
Panchal, J.H., Kalidindi, S.R., McDowell, D.L., Comput. Aided Des. 45, 4 (2013).Google Scholar
McDowell, D.L., Kalidindi, S.R., MRS Bull. 41, 326 (2016).CrossRefGoogle Scholar
Donoho, D., “50 Years of Data Science,” http://courses.csail.mit.edu/18.337/2015/docs/50YearsDataScience.pdf (accessed March 9, 2016).Google Scholar
Anderson, C., “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete” (updated 6/23/2008), available at http://www.wired.com/science/discoveries/magazine/16-07/pb_theory.Google Scholar
Hey, A.J., Tansley, S., Tolle, K.M., “The Fourth Paradigm: Data-Intensive Scientific Discovery” (Microsoft Research, Redmond, WA, 2009).Google Scholar
Wenger, E., Communities of Practice: Learning, Meaning, and Identity (Cambridge University Press, New York, 1998).Google Scholar
The R Project for Statistical Computing, http://www.r-project.org (accessed March 9, 2016).Google Scholar
SciPy: Open Source Scientific Tools for Python, http://www.scipy.org (accessed March 6, 2016).Google Scholar
Walt, S.V.D., Colbert, S.C., Varoquaux, G., Comput. Sci. Eng. 13 (2), 22 (2011).CrossRefGoogle Scholar
Scikit Learn, http://scikit-learn.org/stable (accessed March 6, 2016).Google Scholar
Seabold, S., Perktold, J., Proc. 9th Python Sci. Conf., pp. 57–61 (2010).Google Scholar
McKinney, W., Proceedings of the 9th Python in Science Conference (2010), pp. 51–56.Google Scholar
MATLAB User’s Guide 5, 333 (Natick, MA, 1998).Google Scholar
Project Jupyter, http://jupyter.org/index.html (accessed March 12, 2016).Google Scholar
Galaxy, https://galaxyproject.org (accessed March 6, 2016).Google Scholar
Pegasus, https://pegasus.isi.edu (accessed March 6, 2016).Google Scholar
KNIME, https://www.knime.org (accessed March 6, 2016).Google Scholar
Orange, http://orange.biolab.si (accessed March 6, 2016).Google Scholar
gUSE—Grid and Cloud Support User Environment, http://guse.hu (accessed March 12, 2016).Google Scholar
e-Science Gateways, http://www.esciencecentral.org/gateways.php (accessed March 6, 2016).Google Scholar
HUBzero, https://hubzero.org (accessed March 6, 2016).Google Scholar
Nanohub, https://nanohub.org (accessed March 6, 2016).Google Scholar
Krein, M.P., Natarajan, B., Schadler, L.S., Brinson, L.C., Deng, H., Gai, D., Li, Y., Breneman, C.M., “Development of Materials Informatics Tools and Infrastructure to Enable High Throughput Materials Design,” Mater. Res. Soc. Symp. Proc. 1425 (Materials Research Society, Warrendale, PA, 2012).Google Scholar
Breneman, C.M., Brinson, L.C., Schadler, L.S., Natarajan, B., Krein, M., Wu, K., Morkowchuk, L., Li, Y., Deng, H., Xu, H., Adv. Funct. Mater. 23, 5746 (2013).CrossRefGoogle Scholar
Kalidindi, S.R., Gomberg, J.A., Trautt, Z.T., Becker, C.A., Nanotechnology 26, 344006 (2015).Google Scholar
Cebon, D., Ashby, M.F., MRS Bull. 31, 1004 (2006).CrossRefGoogle Scholar
Agrawal, A., Deshpande, P.D., Cecen, A., Basavarsu, G.P., Choudhary, A.N., Kalidindi, S.R., Integr. Mater. Manuf. Innov. 3, 1 (2014).CrossRefGoogle Scholar
Liu, Z.-K., Chen, L.-Q., Rajan, K., JOM 58, 42 (2006).Google Scholar
Rajan, K., Mater. Today 8, 38 (2005).Google Scholar
Bhadeshia, H.K.D.H., Stat. Anal. Data Min. 1, 296 (2009).Google Scholar
Kalidindi, S.R., Niezgoda, S.R., Landi, G., Vachhani, S., Fast, T., CMC Comput. Mater. Con. 17, 103 (2010).Google Scholar
Steinmetz, P., Yabansu, Y.C., Hötzer, J., Jainta, M., Nestler, B., Kalidindi, S.R., Acta Mater. 103, 192 (2016).CrossRefGoogle Scholar
Gupta, A., Cecen, A., Goyal, S., Singh, A.K., Kalidindi, S.R., Acta Mater. 91, 239 (2015).CrossRefGoogle Scholar
Yabansu, Y.C., Kalidindi, S.R., Acta Mater. 94, 26 (2015).CrossRefGoogle Scholar
Cecen, A., Fast, T., Kumbur, E.C., Kalidindi, S.R., J. Power Sources 245, 144 (2014).Google Scholar
Niezgoda, S.R., Kanjarla, A.K., Kalidindi, S.R., Integr. Mater. Manuf. Innov. 2 (3), (2013), doi:10.1186/2193-9772-2-3.Google Scholar
Kalidindi, S.R., ISRN Mater. Sci. 2012, 305692 (2012).Google Scholar
Fast, T., Kalidindi, S.R., Acta Mater. 59, 4595 (2011).Google Scholar
Shaffer, J.B., Knezevic, M., Kalidindi, S.R., Int. J. Plast. 26, 1183 (2010).CrossRefGoogle Scholar
Citrine Informatics, http://www.citrination.com (accessed March 6, 2016).Google Scholar
Clean Energy Project, http://cleanenergy.molecularspace.org (accessed March 6, 2016).Google Scholar
Curtarolo, S., Setyawan, W., Hart, G.L., Jahnatek, M., Chepulskii, R.V., Taylor, R.H., Wang, S., Xue, J., Yang, K., Levy, O., Comput. Mater. Sci. 58, 218 (2012).Google Scholar
CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry), http://www.calphad.org (accessed March 6, 2016).Google Scholar
MatWeb, http://www.matweb.com (accessed March 6, 2016).Google Scholar
NIST (National Institute of Standards and Technology) Data Gateway, http://srdata.nist.gov/gateway/gateway?dblist=1 (accessed March 6, 2016).Google Scholar
NIST Material Measurement Laboratory, http://www.ctcms.nist.gov/potentials (accessed March 6, 2016).Google Scholar
Open Quantum Materials Database, http://oqmd.org (accessed March 6, 2016).Google Scholar
Granta Design, Data Products, http://www.grantadesign.com/download/pdf/Data-products.pdf (accessed March 6, 2016).Google Scholar
MatNavi (NIMS Materials Database), http://mits.nims.go.jp/index_en.html (accessed March 6, 2016).Google Scholar
Becker, C.A., Tavazza, F., Trautt, Z.T., de Macedo, R.A.B., Curr. Opin. Solid State Mater. Sci. 17, 277 (2013).Google Scholar
Tadmor, E.B., Elliott, R., Sethna, J., Miller, R., Becker, C., JOM 63, 17 (2011).Google Scholar
Kaufman, J.G., Begley, E.F., Adv. Mater. Proc. 161, 35 (2003).Google Scholar
Varde, A.S., Begley, E.F., Fahrenholz-Mann, S., Proc. 4th Int. Workshop Data Mining Standards, Services and Platforms (American Association for Computing Machinery, New York, 2006), pp. 47–54.Google Scholar
Chirico, R.D., Frenkel, M., Diky, V.V., Marsh, K.N., Wilhoit, R.C., J. Chem. Eng. Data 48, 1344 (2003).Google Scholar
MDCS—Materials Data Curation System, GitHub, https://github.com/usnistgov/MDCS (accessed March 9, 2016).Google Scholar
Materials Commons, http://www.prisms-center.org/#/mcommons/overview (accessed April 15, 2016).Google Scholar
Chance, S., Paul, C., Adv. Mater. & Proc. 174, 25 (2016).Google Scholar
Brown, W.F., J. Chem. Phys. 23, 1514 (1955).Google Scholar
Torquato, S., Random Heterogeneous Materials (Springer-Verlag, New York, 2002).Google Scholar
Adams, B.L., Kalidindi, S.R., Fullwood, D., Microstructure Sensitive Design for Performance Optimization (Butterworth-Heinemann, Oxford, 2012).Google Scholar
Fullwood, D.T., Niezgoda, S.R., Adams, B.L., Kalidindi, S.R., Prog. Mater. Sci. 55, 477 (2010).Google Scholar
Milton, G.W., The Theory of Composites (Cambridge University Press, Cambridge, 2002).Google Scholar
PyMKS: Materials Knowledge System in Python, figshare, http://dx.doi.org/10.6084/m9.figshare.1015761 (accessed March 17, 2016).CrossRefGoogle Scholar
ResearchGate, https://www.researchgate.net (accessed March 12, 2016).Google Scholar
LinkedIn, https://www.linkedin.com (accessed March 12, 2016).Google Scholar
Ahrens, J., Geveci, B., Law, C., Hansen, C., Johnson, C., The Visualization Handbook (Elsevier Butterworth–Heinemann, New York, 2005), pp. 717–731.Google Scholar
TomViz for Tomographic Visualization of Nanoscale Materials, TomViz, http://www.tomviz.org (accessed March 9, 2016).Google Scholar
Avizo 3D Software for Materials Science, Avizo, http://www.fei.com/software/avizo-3d-for-materials-science. Accessed March 9, 2016.Google Scholar
Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G., Proc. Eurographics Italian Chap. Conf. 2008 (2008), pp. 129–136.Google Scholar
ABAQUS/Standard: User’s Manual (Hibbitt, Karlsson & Sorensen, Inc., Pawtucket, RI, 1998).Google Scholar
ANSYS Academic Research, Release 17.0, Help System (ANSYS Inc., 2016).Google Scholar
Wolfram, S., Mathematica: A System for Doing Mathematics by Computer (Addison Wesley Longman, Reading, PA, 1991).Google Scholar
Uschold, M., Gruninger, M., Knowl. Eng. Rev. 11, 93 (1996).Google Scholar
Chandrasekaran, B., Josephson, J.R., Benjamins, V.R., IEEE Intell. Syst. 14, 20 (1999).Google Scholar
MATIN: e-Collaboration Platform for Materials Innovation, http://materials.gatech.edu/matin (accessed March 12, 2016).Google Scholar
Institute for Materials at Georgia Institute of Technology, http://materials.gatech.edu (accessed March 10, 2016).Google Scholar
Koizumi, Y., Kobayashi, T., Yokokawa, T., Zhang, J., Osawa, M., Harada, H., Aoki, Y., Arai, M., Superalloys 2004, 35 (2004).Google Scholar
Nickel Based Superalloy Design Model, http://mined.gatech.edu/ni_super_alloy_design/ (accessed June 19, 2016).Google Scholar
Generic Materials Design Toolkit, https://mgi.nist.gov/generic-materials-design-toolkit (accessed June 19, 2016).Google Scholar
Materials Knowledge Systems in Python, http://pymks.org (accessed June 19, 2016).Google Scholar